Tropical cyclone prediction system and method

ABSTRACT

A method of predicting information related to a path of a weather phenomenon includes obtaining a plurality of tracks corresponding to the weather phenomenon from at least one source. A factor is assigned to each of the plurality of tracks. A set of probabilities for the weather phenomenon to intersect a plurality of segments corresponding to a boundary is determined using at least intersection points of the plurality of tracks with the boundary and the factor assigned to each of the plurality of tracks.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.12/148,390, entitled Tropical Cyclone Prediction System and Method,filed Apr. 18, 2008, and U.S. patent application Ser. No. 12/148,402,entitled Tropical Cyclone Prediction System and Method, filed Apr. 18,2008, the entire disclosures of which are incorporated herein byreference.

COPYRIGHT NOTICE AND AUTHORIZATION

Portions of the documentation in this patent document contain materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice file or records, but otherwise reserves all copyright rightswhatsoever.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description will be better understood when readin conjunction with the appended drawings, in which there is shown oneor more of the multiple embodiments of the present invention. It shouldbe understood, however, that the various embodiments of the presentinvention are not limited to the precise arrangements andinstrumentalities shown in the drawings.

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

In the Drawings:

FIG. 1 is a use case diagram for the tropical cyclone prediction systemin accordance with an embodiment of the present invention;

FIG. 2 is a flow diagram for the tropical cyclone prediction system inaccordance with an embodiment of the present invention;

FIG. 3 is a representation of exemplary model tracks for tropicalcyclones in accordance with the tropical cyclone prediction system ofFIG. 1;

FIGS. 4A-4C are representations of exemplary model tracks for a tropicalcyclone in accordance with the tropical cyclone prediction systems ofFIG. 1;

FIG. 5 is a graphical representation of boundary intersection functionsin accordance with the tropical cyclone prediction system of FIG. 1;

FIG. 6 is a representation of actual model tracks for two differenttropical cyclones in accordance with the tropical cyclone predictionsystem of FIG. 1;

FIG. 7 is a time sequence of mean forecast tracks for two differenttropical cyclones in accordance with the tropical cyclone predictionsystems of FIG. 1;

FIG. 8 is a graphical representation of boundary intersection functionsin accordance with the tropical cyclone prediction system of FIG. 1;

FIG. 9 is a network diagram that includes the tropical cycloneprediction system of FIG. 1; and

FIG. 10 is a block diagram of a computer system for realization of thetropical cyclone prediction system of FIG. 1.

DETAILED DESCRIPTION

Certain terminology is used herein for convenience only and is not to betaken as a limitation on the present invention. In the drawings, thesame reference letters are employed for designating the same elementsthroughout the several figures.

The words “right”, “left”, “lower” and “upper” designate directions inthe drawings to which reference is made. The words “inwardly” and“outwardly” refer to directions toward and away from, respectively, thegeometric center of the tropical cyclone prediction system anddesignated parts thereof. The terminology includes the words abovespecifically mentioned, derivatives thereof and words of similar import.

Unified Modeling Language (“UML”) can be used to model and/or describemethods and systems and provide the basis for better understanding theirfunctionality and internal operation as well as describing interfaceswith external components, systems and people using standardizednotation. When used herein, UML diagrams including, but not limited to,use case diagrams, class diagrams and activity diagrams, are meant toserve as an aid in describing the embodiments of the present invention,but do not constrain implementation thereof to any particular hardwareor software embodiments. Unless otherwise noted, the notation used withrespect to the UML diagrams contained herein is consistent with the UML2.0 specification or variants thereof and is understood by those skilledin the art.

Hereafter, relative to the discussion of the tropical cyclone predictionsystem, the term tropical cyclone refers to tropical storms, hurricanes,typhoons, and any other rotating storm systems of tropical natureunderstood by one of ordinary skill in the art. The methods describedbelow, while generally discussed and exemplified herein using hurricanesoriginating in the Northern Atlantic basin, are not limited to cyclonicstorms in the region, but are applicable to tropical cyclonesoriginating in any of the waters of the world. Furthermore, the methodsdescribed below should not be considered as limited to embodiments fortropical cyclones, but may be applied to any weather phenomenon. Weatherphenomena include any type of storm, including tropical cyclones,convective storms, such as thunderstorms and thunderstorm systems,tornado, winter storms, fronts, and any other type of storm, stormsystem, weather event, weather system, or weather pattern recognized byone skilled in that art.

Accurate forecasting of tropical cyclones is critical for the assessmentof the risk of losses due to tropical cyclones as well as civic planningof evacuations and emergency preparations for those locations mostlikely affected by the tropical cyclones. Forecast centers, one exampleof which is the National Centers of Environmental Prediction (NCEP),provide periodic updates of expected tropical cyclone tracks by runningcomplex models utilizing the more recent observational data. Theseupdates include a range of potential locations of the center ofcirculation of the tropical cyclone as a function time for some periodof time. For example, the National Hurricane Center (NHC), which is acenter within the NCEP, only disseminates hurricane forecasts out tofive days. Since NHC and other centers only produce forecasts for theshort term, the public is left with little guidance on which areas willmost likely be affected by the tropical cyclone when it is still farfrom land. Further, the NHC forecast “cone” of uncertainty is basedsolely on historical forecast errors rather than the predictability ofthe current tropical cyclone, which can vary significantly from case tocase. The tropical cyclone prediction system (TCPS) described hereinpredicts tracks and characteristics of tropical cyclones as well asproviding quantitative measures of the uncertainty of those predictions,which is especially important at forecast lead times beyond the typicalfive days provided by the NCEP and other forecast centers.Characteristics include, but are not limited to intensity, minimumcentral pressure, wind speed, storm surge height, radius of impact(e.g., extent of hurricane force winds), precipitation rate, and tornadopotential. By intelligently combining model data obtained from variousglobal meteorological models, the TCPS can provide tropical cycloneforecasts for many days beyond what is currently available from theNational Centers of Environmental Prediction or other tropicalforecasting centers around the world.

The forecast data provided by the TCPS may be useful to a variety ofdifferent sectors. The insurance industry can combine the forecast datawith landfall damage models to assess potential insured losses by takinginto account the intensity at landfall and the insured value of propertyin the expected landfall region. Local, state, and federal emergencymanagement agencies can utilize the forecast data from the TCPS to ordermandatory evacuations with a lower probability of false alarms.Emergency shelter preparation and large scale staging of resources andsupplies can be also facilitated with greater confidence where thoseresources are most needed.

FIG. 1 is a use-case diagram for the tropical cyclone prediction system100 and associated systems and actors in accordance with one embodimentof the present invention. Referring to FIG. 1, the tropical cycloneprediction system 100 includes an obtain ensemble data use case 120 thatthe TCPS 100 utilizes to obtain the most current ensembles of modeltracks from various forecast centers 110, which provide periodic updateson the actual and expected path and intensity of tropical cyclones. TheTCPS 100 may also obtain short range forecast model data using an obtainpredictive track use case 127. The short range model data may bedetermined using models obtained from the weather database 610, oralternately stored on an internal database associated with the TCPS 100.A forecaster 140 may provide input into the TCPS 100 using an obtainhuman influenced track data use case 129. An assign scaling factors usecase 126 associates a scaling factor for each model track of theensemble data received from the forecast centers 110. The assign factorsuse case 126 is extended by a manage scaling factors use case 124. Themanage scaling factors use case 124 determines the scaling factors bycomparing and analyzing actual data for a tropical cyclone with themodel data from the forecast centers 110. The actual data for thetropical cyclones is obtained from one or more weather databases 610using an obtain historical data use case 122. The assign scaling factorsuse case 126 is further extended by an identify tropical cycloneinstances use case 123. The identify tropical cyclone instances use case123 is used to identify new instances of tropical cyclones that arepredicted to form after an initialization time associated with thecurrent forecast period for the models used to generated the ensemble ofmodel tracks from the forecast center 110. The TCPS 100 utilizes apredict BIF use case 128, which includes the assign scaling factors usecase 126, to aggregate the scaled model tracks and determine boundaryintersection functions (described below) for the predicted landfallposition and characteristics of the tropical cyclone, such as wind speedor intensity. The boundary intersection functions are made available toan entity, such as a user 668 or subscriber 671 using a distribute datause case 130 which includes the predict BIFs use case 128. Displaymechanisms and human-machine interfaces for distribution of the TCPS 100output data are generally known in the art, and should not be consideredlimiting. Furthermore, the entity may be an computer system or networkdesigned to interact with the TCPS 100, such as an internet tropicalcyclone prediction server 664.

Referring to FIG. 2, in order for the TCPS 100 to provide boundaryintersection functions associated with the tracks and characteristics oftropical cyclones, ensemble data of model tracks for a tropical cyclonegenerated by forecast centers are received 150. The TCPS 100 assignsscaling factors to each track of the ensemble data 160 and determinesboundary intersection functions for the track and characteristics of thetropical cyclone from the aggregated model tracks and the associatedscaling factor assigned to each 170. The scaling factors are refined orupdated 165 based on comparing the actual track and actualcharacteristics of a tropical cyclone with the tracks andcharacteristics predicted by the ensemble data from the various forecastcenters. The boundary intersection functions and other data provided bythe TCPS 100 are distributed to users and other external entities 180.In one embodiment the boundary intersection function for the track of atropical cyclone is for landfall of the tropical cyclone as a functionof distance along a geographic boundary, such as a coastline.

The TCPS 100 utilizes model data obtained from forecast centers. Someexamples of forecast centers include, but are not limited, to theNational Centers of Environmental Prediction (NCEP), European Centre forMedium-Range Weather Forecasts (ECMWF), and Environment Canada (EC). Asone skilled in the art will recognize, model data from other forecastingcenters, both government sponsored or privately funded, may be utilizedby the TCPS 100. The forecast centers typically provide updated modeldata every 12 hours, although one skilled in the art will recognize thatthe frequency could be greater or less than twice daily. The forecastcenter model data spans a period of time starting at the initializationtime for the model runs and ending at a termination time for the modelruns. For example, each model run may span a period of time of 240 hours(10 days) from the initialization time of the model run, although it isunderstood by one skilled in the art that the time period spanned by themodel run may be greater than or less than 240 hours. The model datafrom the forecast centers consists of an ensemble of model tracksproduced using sophisticated atmospheric models. One forecast centermodel run produces a model track for the tropical cyclone based on theinitial conditions input into the model, where the model track projectsthe path and intensity of the tropical cyclone. By perturbing theinitial conditions based on estimated uncertainties in those conditions,a set of possible model tracks, or an ensemble, for the tropical cycloneis created. Thus, each of the forecast centers produces an ensemble ofmodel tracks to provide a range of potential paths for the tropicalcyclone based on small perturbations of the initial conditions. Atypical ensemble may contain 10-60 different paths obtained from varyingthe initial conditions of the model, although it is understood by oneskilled in the art that the range 10-60 should not be consideredlimiting (i.e., an ensemble may contain more than 60 model tracks orless than 10 model tracks).

Each model track may span a maximum time period corresponding to theinitialization time for the model run and the termination time for themodel run, although a model track may span a smaller time period if themodel run indicates the tropical cyclone dissipating before the end ofthe model run, or conversely, the model run indicates the tropicalcyclone forming after the initialization time for the model run.Referring to FIG. 3, exemplary model tracks 220, 222, 224, and exemplarymodel track 230 are shown for exemplary tropical cyclones 205 and 210,respectively. On each model track, an indication of time from theinitialization time, T0, is shown by the horizontal bars intersectingeach of the model tracks. The time span of the model run for theseexemplary model tracks is 10 days, as indicated by the termination ofeach model track at T10. It should be noted that each time indicator forall the models tracks is not labeled in FIG. 3, although both modeltracks 224 and 230 have a complete set of time indicators labels.Tropical cyclone 205 has been identified to have formed at or prior toT0 since model tracks exist for tropical cyclone 205 at the T0initialization time. Tropical cyclone 210 does not have a correspondingmodel track for the initialization time T0 of the model run, instead itpredicted to form around time T7 of the model run. Model tracks 222 and224 are shown to cross a boundary 200 at time ranges T8-T9 and T7-T8,respectively. Model track 220 does not intersect the boundary 200 duringthe time span of the model run.

For each forecast period, the TCPS 100 verifies each instance of atropical cyclone based on the model data obtained from the forecastcenters. By comparing the ensemble of model tracks for successiveforecast periods, the TCPS 100 can correlate an instance of a tropicalcyclone identified from a previous forecast period with model tracks forthe current forecast period. If the TCPS 100 cannot correlate any modeltracks from the current forecast period with an instance of a previouslyidentified tropical cyclone, the TCPS 100 discontinues providing outputdata related to the track and characteristics of the tropical cyclone,presumably because the tropical cyclone has either dissipated or posesno further threat to economic or popular interests. The TCPS 100 mayalso identify new instances of a tropical cyclone if the current modeldata contains model tracks that cannot be correlated with a previouslyidentified instance of a tropical cyclone. For example, referring againto FIG. 3, tropical cyclone 210 is predicted to form near time T7 for aforecast period. If a previous forecast period (not shown) does notinclude any tropical cyclone instances that can be associated with themodel track 230, then the TCPS 100 predicts a new tropical cycloneinstance for 230 during the current forecast period.

The TCPS 100 may also augment the ensemble of model tracks obtained fromthe forecast centers 110 with predictive track data obtained fromsources other than the forecast centers 110. The predictive track datais obtained using models well understood in the art, as well asincorporating human influence forecast data. These models may beretrieved from a weather database 610 or alternately stored in adatabase internal to the TCPS 100. For example, the predictive trackdata may be obtained using a number of different short range (0-5 day)tropical prediction models where the predictive tracks obtained usingthose models have demonstrated a higher correlation with the actualtracks of tropical cyclones for time periods just after the formation ofthe tropical cyclone (e.g., within the first five days after theformation of the tropical cyclone), but typically show a greaterdivergence than the ensemble model tracks for times farther from theformation of the tropical cyclone (e.g., more than five days after theformation of the tropical cyclone). Examples of these short-rangetropical prediction models include the Geophysical Fluid DynamicLaboratory (GFDL) and Hurricane Weather Research Forecast (HWRF) models.In one embodiment, the ensemble model and predictive track data utilizedby the TCPS 100 is influenced by human forecast experience and/orintuition. For example, for situations where the ensemble model andpredictive model tracks are known to be unreliable, a human forecastermay provide a determination as to which of the ensemble model orpredictive tracks are used by the TCPS 100 based on experience andintuition of how a tropical cyclone is expected to track.

The TCPS 100 receives the most recent ensembles of model tracks from theforecast centers 110. Each track is assigned a scaling factor by theTCPS 100. The scaling factors provide a measure of the skill for whichthe forecast center is known for producing model tracks compared withthe actual path of the tropical cyclone. In an embodiment, higherscaling factors are generally indicative of greater skill. The actualpath which tracks the center of circulation of the tropical cyclone isoften referred to as the actual “best track”. The scaling factors may bedetermined by the TCPS 100 by analyzing and comparing the model tracksof the forecast centers 110 with actual tracks of the tropical cyclones.For example, historical comparison of model tracks produced by the NCEPvs. EC may demonstrate that the ensemble data for the NCEP is morelikely to predict the actual best track of the hurricane. By using thescaling factors, the TCPS 100 can aggregate model tracks from any numberof sources, and effectively rank the data so the historically moreaccurate sources of model tracks have greater effect on the TCPS 100output. In an embodiment, the scaling factors determined by the TCPS 100for each forecast center are dependent on the location, time of year, orcharacteristics of the tropical cyclone. For example, the ECMWF may bemore skillful at modeling tropical cyclones early in their lifecycle,while the NCEP produces more skillful ensembles of model tracks as thetropical cyclone approaches a land mass, or the ECMWF is more skillfulfor tropical cyclones over the Atlantic Ocean, with NCEP more likely toproduce accurate model tracks of Gulf tropical cyclones. As anotherexample, the EC may provide model tracks that historically track theactual track for a tropical cyclone in June, while the model tracks forthe NCEP more closely match the actual tropical cyclone tracks inSeptember. Thus, for each forecast center, the TCPS 100 may have storedin a database, a list of scaling factors dependent on absolute location(e.g., geographic coordinates), relative location with respect to aboundary (e.g. distance from a coastline), time of year, atmospheric,oceanic, and land-surface conditions (e.g., wind shear, sea surfacetemperatures), or characteristics of the tropical cyclones themselves.Therefore, the TCPS 100 may assign a different scaling factor for theensemble of model tracks from one of the forecast centers 110 for thesame tropical cyclone for a different forecast period as the location orcharacteristics of the tropical cyclone change. In an embodiment, forany one forecast period, different scaling factors may be assigned todifferent ensemble tracks obtained from the same forecast center. As anexample, due to the complexities of the atmospheric models run by theforecast centers 110, and their sensitivities to initial atmosphericconditions, a small number of ensemble model tracks may be produced thatare considered outliers (e.g., a model track that does not agree withthe general trending of most of the ensemble track, or produces a paththat is highly improbably or near impossible for an actual tropicalcyclone to follow). These outliers may be assigned an appropriatescaling factor such that their contributions to the output provided bythe TCPS 100 are negligible or completely ignored. In an alternateembodiment, the scaling factors are time-varying. Since the positionsalong the model tracks represent the time evolution of the coordinatefor the center of the tropical cyclone, movement along the model trackbetween any two points corresponds to a range in time. The TCPS 100 mayassign different scaling factors to a model track for positions alongthe model track. Since each position along the model track represent adifferent time in the evolution of the tropical cyclone, the set offactors for the different positions along the model track arerepresentative of a time-varying factor. Referring again to FIG. 3, forthe time ranges T0-T1, T1-T2, . . . , T9-T10 of model track 224, theTCPS 100 may potentially assign a different scaling factor to one,several, or every time range of the model track. As a result, each modeltrack can have a time varying statistical weight, embodied in thescaling factors, that allows different time ranges within the same modeltrack to have a different contribution to the output provided by theTCPS 100. The use of multiple scaling factors within a single modeltrack allows the TCPS to increase contribution to the output of the TCPS100 for time ranges where the model data is determined to have a highskill level, and also decrease or eliminate the contribution of timeranges where the model data is known to have low skill level.

As the different forecast centers 110 revise and refine the internalmodels used to generate the model data, it is expected that the skilllevel of the various forecast centers 110 in forecasting the tracks andcharacteristics of tropical cyclones will change over time. Therefore,the scaling factors are periodically refined or updated by comparing theensemble model data for each of the forecast centers 110 with historicaldata for tropical cyclones. The historical tropical cyclone data may beobtained from an external weather database 610. The historical tropicalcyclone data includes any data related to the actual value of tracks orcharacteristics of tropical cyclones over their entire lifecycles, aswell as corresponding atmospheric, oceanic, and land-surface datarelated to the tropical cyclones. In one embodiment, the scaling factorsare refined and updated after each forecast period by comparing the mostrecent model tracks with the current actual track of the tropicalcyclone.

The predictive track data, including the human influence track data(described above) is easily assimilated into the TCPS 100 with atreatment of the predictive track data the same as that of the ensemblemodel tracks obtained from the forecast centers 110, but the predictivetracks are assigned a disproportionally high scaling factor with respectto the ensemble model tracks, resulting in the predictive data tracksstatistically dominating the output of the TCPS 100 for the track andcharacteristics of the tropical cyclone. For example, it is know thatPredictive Models A, B, and C generally provide a more accurate trackmore indicative of the actual track of a tropical cyclone for the first12 hours after the formation of the tropical cyclone than any of theensemble model tracks obtained from the forecast centers 110. For asituation where the tropical cyclone is expected to make landfall withinthat 12 hour period, the predictive tracks from Models A, B, and C areassigned a factor greater than any factor assigned to the ensemble modeltracks. The probabilities and BIFs determined by the TCPS 100 are thenmost greatly influenced by the predictive tracks from Models A, B, andC. With respect to the discussion, it is understood that ensembles ofmodel tracks utilized by the TCPS 100 to determine probabilities andBIFs for the track and characteristic of a tropical cyclone may includeboth predictive tracks and human influenced tracks without affecting theoperation of the TCPS 100 as described hereafter.

The TCPS 100 then uses the scaled ensembles of model tracks to provide aprobability that the tropical cyclone will intersect different sectionsof a boundary. In one embodiment, the boundary is a geographic boundary,such as a coastline, and the intersection point with the boundaryrepresents the location of landfall of the center of the tropicalcyclone. The boundary is divided in number of segments. The length ofthe segments may all be the same. For example, a 1000 mile long sectionof coastline may be divided into 10 equal segments of 100 miles.Alternately, some or all of the segments may be of unequal length, forexample, the coastline may be sectioned into 8 segments, where themiddle four segments are each 100 miles, and the remaining four segmentsare each 150 miles. Each segment might also be a different length, wherethe segments are formed based on population centers or concentrations ofwealth. Referring to FIGS. 4A-4C, an exemplary coastline 300 is dividedinto seven equal segments, with one exemplary segment labeled 302.

While the TCPS 100 has no limitations on the number of segments alongthe boundary (other than the number be a positive integer) or the lengthof any segment (other than the value be a positive number), there is apractical range for the segments and lengths based on physical scale oftropical cyclones in relation to the boundary, the total number ofensemble tracks available, and the need to provide useful information tothe users of the system. For example, if the segments are too short, orthere are too many segments, the number of ensemble tracks intersectingeach segment may be too low to produce a meaningful probabilityestimates for each of the segments. If the segments are too large, theprobability that the tropical cyclone intersects that boundary does notconvey adequate information about where along that boundary the mostsevere effects of the tropical cyclone will be experience. Thegeographic boundary is not limited to a physical boundary, such as thepreviously mentioned coastline. It could be a construct such as a lineof latitude or longitude. In one embodiment, the probabilities that thetropical cyclone will intersect different sections of a boundary aredetermined for boundaries other than a coastline or for conditions otherthan expected landfall since data can be extracted from the modelsolutions at any arbitrary boundary since complete time evolution of thetropical cyclone is received in the ensemble forecasts.

For each scaled ensemble model track, the segment where the tropicalcyclone is predicted to intersect the boundary is determined. The methodof determining the intersection point along the boundary of one of theensemble tracks is well understood in the art and omission herein shouldnot be considered limiting. For each segment along the boundary asegment value is calculated based on the weighted average of the numberof tracks intersecting that boundary, where the weight used for eachmodel track is the scaling factor previously assigned to the model trackby the TCPS 100. Model tracks that do not intersect the boundary areaggregated together, and a segment value is calculated. Probabilities ofthe tropical cyclone intersecting each boundary are determined using thesegment value for each boundary divided by the sum of the segment values(including the segment value of the non-intersecting model tracks). Forthe case where each model track intersects the boundary, the sum of theprobabilities for each segment of the boundary is one (e.g., there is100% chance of landfall of the tropical cyclone along the entire lengthof the coastline). Where there are model tracks that do not intersectthe boundary, the sum of probabilities for each segment of the boundaryis less than one (i.e., the tropical cyclone has a finite probability ofnot making landfall along any section of the coastline).

The set of probabilities for the segments along the entire boundary canbe graphically represented as a boundary intersection function (BIF).The boundary intersection function is similar to a probabilitydistribution function (PDF), which is well understood in the art. Theboundary intersection function differs from the PDF in that a BIF is notrequired to have an integrated area under the BIF equal to one, anexample of which is described above where some of the model tracks for atropical cyclone do not intersect any segment along the boundary whichis indicative of less then 100% certainty that the tropical cyclone willintersect the boundary (e.g., the tropical cyclone may turn and remainover the water for its entire lifecycle. Referring collectively to FIGS.4A-4C and FIG. 5, exemplary boundary intersection functions 315, 318,320 represent the probability of landfall of tropical cyclone 322, 324,326 along a section of coastline 300 corresponding to exemplary suitesof model tracks 323, 325, 327. The probability of landfall isrepresented on the y-axis 305 and the position along the coastline onthe x-axis 310. The x-axis 310 is partitioned into seven sectionscorresponding to the seven segments in coastline 300 in FIGS. 4A-4C.Note that positions “A” and “B” along coastline 300 in FIGS. 4A-4Ccorrespond to positions “A” and “B” along x-axis 310 in FIG. 5. Itshould be noted that each of the BIFs 315, 318, 320 in FIG. 5 include adiscrete set of points, which are indicative of the exemplaryprobability for landfall along the coastline for each of the segments asdetermined by the TCPS 100. The discrete points may be represented as acontinuous curve, such as in FIG. 5, using well known mathematicaltechniques.

Referring to FIG. 5, the boundary intersection function 315 representsthe probability of the tropical cyclone 322 making landfall over thecoastline 300 for an exemplary suite of five model tracks collectivelylabeled 323. The peak of the BIF 315, corresponding to location “A”represents the segment of most probable landfall along the coastline300, while the width of the distribution 330 represents a measure of theuncertainty of the landfall prediction for the tropical cyclone 322.Similarly, the boundary intersection function 320 represents theprobability of the tropical cyclone 324 making landfall over thecoastline 300 for an exemplary subset of tracks collectively labeled 325in FIG. 5. The peak of the BIF 320, corresponding to location “B”represents the segment of most probable landfall along that section ofcoastline 300, while the width of the distribution 331 represents ameasure of the uncertainty of the landfall prediction for the tropicalcyclone 324. Uncertainty measurement using the widths of mathematicalfunctions is well known in the art and the description thereof hereinshould not be considered limiting. Comparing the widths 330, 331 withthe spread in the model tracks 323, 325, respectively, shows the spreadin the potential landfall points for the different ensemble trackscorrelated to the width of the corresponding BIF. In one embodiment,BIFs 315 and 320 may represent two different times for the same tropicalcyclone (i.e., tropical cyclones 322, 324 are the same tropical cycloneand the suites of tracks 323 and 325 represent model solution at twodifferent times). If tropical cyclone 322 represents a first time T1 andtropical cyclone 323 represents a second time T2, BIF 315 indicates amost probable landfall for time T1 near A with a first uncertainty,corresponding to the width 330, and BIF 320 indicates a most probablelandfall for time T2 at near location B with a second uncertainty,corresponding to the width 331, where the uncertainty in the landfallposition at time T2 is less than the uncertainty in the landfallposition at time T1.

The area under a BIF represents the total probability that a tropicalcyclone will make landfall along a particular geographic boundary,normalized to one when landfall is certain to occur at some point alongthat geographic boundary. For example, referring again to FIGS. 4A-4Cand 5, for tropical cyclones 322, 324 where each of the suite of modelruns 323, 325, respectively, indicates the cyclone will intersect thecoastline 300, the area under both of the BIFs 315, 320 is one, but fortropical cyclone 326, with a exemplary suite of model tracks 327, wherethe some of the model tracks predict landfall along coastline 300 andothers predict the tropical cyclone 326 remaining over the ocean for itsentire lifecycle, the area under BIF 318 is less than one. It should benoted that symmetry of BIFs about their peak is not implied in any wayby the BIF representations in FIG. 5. Asymmetry of the BIFs isinfluenced, without limitation, by the shape of the geographic boundaryand the factors assigned to each model track. The exemplary coastline300 represented as a straight line in FIGS. 4A-4C is not to beconsidered limiting, as few coastlines are straight on a scale that ismeaningful to the size of an area potentially impacted by a tropicalcyclone.

Stated another way, the TCPS 100 probability determinations are based onmany possible tracks for each forecast time, and the variance of thesetracks with the scaling factors assigned by the TCPS 100 is proportionalto the forecast uncertainty. Since model tracks might focus narrowly ona single region, fan out over a large area, or cluster around more thanone likely landfall location, users can exploit the relative uncertaintyimplied by the spread of these long-lead forecasts. For example,catastrophe bond traders can establish a more or less risky marketposition when the TCPS 100 shows a greater or lesser amount ofuncertainty at a given time for a given tropical cyclone.

For each forecast period using the scaled ensemble model tracks, theTCPS 100 also produces a most probable track, or mean track, of thetropical cyclone and an estimate of uncertainty of that most probabletrack where the uncertainty is based on the most current observationaldata and ensemble data via the spread of the ensemble tracks. This meantrack is produced from a weighted average of each of the ensemble track,where again the weighting is determined by the scaling factor.

Referring to FIG. 6, the two examples of actual collections of ensemblemodel tracks 335, 345 from the various forecast centers 110 are showndemonstrating varying degrees of uncertainty of the most probablelandfall position of two different tropical cyclones, Katrina and Wilma.The x-axis 350 and y-axis 340 represent longitude and latitude,respectively. Each individual line on the chart represents one modeltrack of one model run from one of the forecast centers 110. The linesare color coded as a function of forecast intensity for each point alongthe model track. Note that the range of potential landfall locations forKatrina spans almost the entire US Gulf Coast, much of the East Coast,while some of the most easterly tracks remaining entirely over theAtlantic Ocean. The spread of ensemble model tracks alerts the user ofthe significant uncertainty of this particular forecast. Decisionsshould then be made not just on the mean track forecast, represented bythe circles in FIG. 6, but on the deviation of the entire suite of modeltracks from the mean track. In contrast, the model tracks for Wilma showa majority of the suite of model solutions had narrowed to a southernFlorida landfall. The narrower spread in the potential model solutionsfor Wilma compared with Katrina demonstrates a higher confidence in theforecast landfall region for Wilma. The TCPS 100 then uses the scaledensembles to produce a most probable track of the tropical cyclone andan estimate of uncertainty of that most probable track where theuncertainty is based on the most current observational data and ensembledata via the spread of the ensemble tracks.

Referring to FIG. 7, the difference in predictability of the same twotropical cyclones can be further illustrated in a time series of meantracks 355, 365, which indicates the most probable track for a tropicalcyclone based on analysis of the suite of ensemble model tracks at anyone forecast period as described above. Such time series 355, 365 foractual tropical cyclones Katrina and Wilma are shown in FIG. 7. Similarto FIG. 6, the x-axis 350 and y-axis 340 represent longitude andlatitude, respectively. Each curve in FIG. 7 represents the mean trackproduced by the TCPS 100 for one forecast period. Note that for eachforecast period, an associated landfall BIF, as described above (and notshown here), is also produced by the TCPS 100. Time series 355 is agraphical representation of the mean track produced by the TCPS 100 forthe five days preceding the eventual landfall of Katrina. The rightmosttrack 356 corresponds to the earliest time in the series, and thecluster of leftmost tracks 357 represent the later times. The mostprobable landfall location for Katrina was largely uncertain for theearly forecast periods as indicated by the shift for each successiveforecast period of most probable landfall location (as indicated by theintersection of the mean track with the coastline) over the entireNorthern Gulf coast. Only at the very later forecast times did the mostprobable landfall locations converge on the New Orleans area (about 3days ahead of landfall in this case). A time sequence of BIF (not shown)would indicate a gradual shift to the left of the peak as timeincreases, in addition to a narrowing of the BIF as the time of landfallapproached, similar to the exemplary BIFs 315, 320 in FIG. 5. Incontrast, the most probable landfall location for Wilma was lessuncertain for the early forecast periods as indicated by the narrowspread of potential landfall locations over the south Florida gulfcoast. Time series 365 is a graphical representation of mean tracksproduced by the TCPS 100 for the eight days preceding the eventuallandfall of Wilma. Even at eight days prior to the eventual landfall ofWilma, the cluster of mean tracks 367 indicate a probable landfalllocation convergence on the southern Florida area. For Wilma, a timesequence of BIF (not shown) would indicate a gradual drifting around thebest track landfall position, where the best track as published by theNHC is indicated by the circles in FIG. 7, as well as a slight narrowingof the BIFs for times nearer to landfall.

As stated above, the NHC forecasts are issued with uncertainty estimates(cones), but this information is not storm-specific, and is insteadbased on mean historical forecast errors. This results in identical conesizes for all tropical cyclones and all forecasts, regardless of thecomplexity of any particular track forecast. In contrast, the TCPS 100incorporates uncertainty estimates that are driven by the actualmeteorological conditions at the time of the forecast, as included inthe ensemble modeling from the forecast centers 110, which can provideimportant information for the users when the various suites of ensembletracks are all converging on the same region many days in the future, asdemonstrated for Wilma in FIGS. 6 and 7.

In addition to the track forecasting, accurate intensity (wind speed)forecasts are crucial to predicting the magnitude of potential damageand insured losses at landfall. The forecast centers 110 also provideatmospheric, oceanic, and land-surface data along the ensemble modeltracks which is received by the TCPS 100. The TCPS 100 uses theatmospheric, oceanic, and land-surface data as an input into apost-processing model that predicts tropical cyclone intensity. Theinputs to the post-processing model include the important parametersthat influence tropical cyclone strength: ocean temperature, passageover land, atmospheric wind shear, and other atmospheric, oceanic, andland-surface conditions. The use of the post-processing model is wellknown in the art as the resolution of the forecast center models used toproduce the ensemble model tracks alone is insufficient to reliablypredict the wind speed near the center of circulation for the tropicalcyclone. The TCPS 100 applies the post-processing model at each positionalong each of the model tracks obtained from the various forecastcenters 110 in order to determine a prediction of the intensity of thetropical cyclone for each position along each of the model tracks.

Similar to the track forecasts, the spread between the various intensityforecasts provides valuable information, since the various realizationswill define the envelope of possibilities, rather than just providing anaverage value. Probabilities for ranges of wind speeds at landfall aredetermined by utilizing the scaled suite of ensemble forecasts. Thescaling factors for the intensity predictions do not need to be the sameas for the track predictions, as it is recognized by one skilled in theart that the skill for which the combination of the various forecastcenter models and the post-processing model predict the wind speed forthe ensemble model track may be quite different than the skill for whichthe forecast center models predict the path of the model tracks. Thewind speed ranges are analogous to the boundary sections for thelandfall forecasts, and the resulting probabilities of wind speeds in agiven range at landfall are obtained in similar manner as discussedabove for the track probabilities. The wind speed at the landfallposition for each model track is first determined. Then, a probabilityfor each wind speed range is determined using a weighted average of thenumber of model tracks falling within that wind speed range. The windspeed is typically used to categorize the intensity of a tropicalcyclone where a discrete intensity level of the tropical cyclonecorresponds to wind speed range. One example is intensity categories inthe North Atlantic Basin use the well known Saffir-Simpson scale, whereeach intensity level corresponds to range of wind speeds. As understoodby those skilled in the art, for different regions and oceans basinsaround the globe, different intensity categories, along withcorresponding wind speed ranges are utilized

Referring to FIG. 8, a exemplary time sequence of BIFs 380, 382, 384 forwind speed at landfall is shown for three different times, where BIF 380is for t1, BIF 382 is for t2, BIF 384 is for t3, and t1<t2<t3. Note forsimplicity, the discrete probabilities for each range of wind speeds isnot shown, only the representative continuous curve obtained from thediscrete points. The y-axis 370 represents the probability of a windspeed at landfall and the x-axis 375 represents the wind speeds. Thepeak value of each of the BIFs 380, 382, 384 represents the mostprobable wind speed at landfall. Again, uncertainty in the wind speedforecast is represented by the BIF widths. Since the total area undereach of the curves is normalized to unity, the relative peak highs andwidths between the BIFs provide an indication of the convergence of theforecast over time. It should be noted that unlike for the landfall BIFsdescribe above, the entire range of potential values for wind speed areplotted on the x-axis 375, therefore the integrated area under any windspeed BIF is unity, where it is possible that the integrated area undera landfall BIF is less than one if some of the model tracks do notintersect the boundary. For example, referring to FIG. 8, theprobability of a particular wind speed at the landfall location isplotted for three different times t1, t2, and t3, represented by BIFs380, 382, 384, respectively. The x-axis 375 is marked with the windspeeds corresponding to transitions between the categories of theSaffir-Simpson scale, with the categories 386 for each of the wind speedranges also noted. For time t1, the most probable wind speed at landfall381, corresponds to a mid category 3 hurricane, while the range ofpossible wind speeds at landfall includes a potential for a intensityranging from strong category 1 to a weak category 5. In other words, theintensity is highly uncertain at time t1. At a time t2, later than t1,the most probable intensity landfall category is now a weak category 3tropical cyclone as indicated by the most probable wind speed 383 of BIF382, with the range of possible intensities predicted to range from weakcategory 2 to strong category 3. At time t3, the sharp peak of BIF 385indicates a high probability of a strong, category 2 tropical cycloneintensity at landfall, with a range of mid category 2 to mid category 3.It should be noted that, in general, intensity BIFs such as those inFIG. 8, do not indicate most likely location of landfall (which isdetermined using the landfall location BIFs described above), so thatthe most probable landfall locations for a tropical cyclone at t1 thought3 are not necessarily the same.

In addition to predicting track and intensity forecasts as describedabove, the TCPS 100 can provide predictions and BIFs for othercharacteristics of a tropical cyclone, including but not limited tominimum central pressure, storm surge, radius of impact (e.g., extent ofhurricane force winds), precipitation rate, and tornado potential, usingthe methods describe above for the intensity and wind speed.

The BIFs produced by the TCPS 100 for any one tropical cyclone arecustomizable according to the needs of users of the data. Thus, for anyone forecast period, the TCPS 100 may provide and distribute manydifferent BIFs for a tropical cyclone, each one determined for adifferent set of user definable parameters. These parameter include, butare not limited to, the number of segments, length of the segments,positioning of the segments, or wind speed ranges. For example,catastrophe bond investors/traders and reinsurance companies may desirea track BIF segmented based on distributions of wealth, whereasemergency management officials may be more interested in a segmentationof the coastline based on population centers, with local agenciesinterested in a smaller area of the coastline than nationalorganizations. The TCPS 100 may also determine the BIFs for boundariesother than a coastline or for times other than expected landfall. BIFscan be extracted from the model solutions at any arbitrary boundary andany arbitrary time since predictions of time evolution of the tropicalcyclone is received in the ensemble forecasts. As an example, shippingcompanies and airlines may be interested in BIFs using non-physicalboundaries such as lines of longitude in order to achieve businessobjectives, such as minimizing late deliveries or maximizing passengercomfort, which could require significant rerouting or rescheduling.

FIG. 9 is a network diagram that includes the TCPS 100. Weather datacollection stations 604 obtain data related to tropical cyclones andother ground and atmospheric conditions at various locations (not shown)including, among other systems, satellite imagery centers that receivedata from satellites, surface weather observation stations, lightningdetection systems, and/or radar processing stations. Additional data mayalso be gathered from vehicles or mobile transmitters/receivers,including aircraft 692, ships 694 and ground transportation 696, alongwith information regarding their locations. Vehicles may transmit,receive, or transmit and receive to and from one of a system oftransmitters and receivers 690. The system may also collect some typesof data from mobile users 684 using handheld or portable devices 682 viaa wireless network 680. Such data may include one or more ofweather-related data, imagery, video, audio, or related positioninformation. Data from each source may be produced in different formats.Such weather-related data may be transferred over a variety of publicand/or private wired and wireless networks 600 generally known in theart, including the Internet, LAN, or other computer-based communicationor information sharing system to one or more weather databases 610 orforecast centers 110. Previously gathered and/or analyzed data may alsobe present in one or more weather databases 610.

In one embodiment, one or more data sources, including the weatherdatabases 610, and forecast centers 110 provide information over thenetwork 600 to the TCPS 100. The TCPS 100 may also contain an internalweather database. Such information may be provided in any format orprotocol generally known in the art, including an extensible markuplanguage (XML) format. The TCPS 100 provides boundary intersectionfunctions and other data related to tropical cyclones as previouslydescribed to the subscriber 671 or user 668.

The tropical cyclone BIFs and other data or information, collectivelyreferred to herein as tropical cyclone forecast data, produced by theTCPS 100 may reside on a PC or server, or distributed servers (notshown). It could use commercial or open source database platforms suchas Oracle, Microsoft SQL Server, MySQL, or PostgreSQL. The TCPS 100 mayprovide external communication through database connections, custominterfaces, or a web application server, or any other communicationsmedium or system generally known in the art.

In one embodiment, the TCPS 100 provides tropical cyclone forecast datato a subscriber system 670 used by a subscriber 671. Examples ofsubscribers include commodity traders, financial brokers, insurance andreinsurance companies, television or network broadcasters, governmentagencies, emergency relief organizations, or any other entity or serviceinterested in obtaining topical cyclone forecast data. The subscribers671 may or may not pay a fee for access to or otherwise obtaining thetropical cyclone forecast data from the TCPS 100. In one embodiment, thedata transfers could be accomplished using the transfer of XML data. Thetropical cyclone forecast data is viewed by the subscriber 671 usingsoftware and hardware tools 672 to navigate through graphical and/ortextual display of the tropical cyclone forecast data and other weatherrelated information supplied by the TCPS 100. The information may alsobe received as an e-mail or instant message indicating qualitative andquantitative information related to the tropical cyclone forecast dataprovided by the TCPS 100. As describe above, the tropical cycloneforecast data may be provided in a customized format for each subscribersystem 670 based on the needs of the subscriber 671.

The information may be displayed graphically showing the differencesbetween the current TCPS 100 forecast data and the previous TCPS 100forecast data (from earlier forecast periods) to aid the subscriber inrapidly assessing any changes in the predicted future conditionspredicted by TCPS 100. For example, the time sequence of mean forecasttracks 355, 365 shown in FIG. 7 is representative of one embodiment ofgraphically displaying difference between current and previous TCPS 100forecast data.

In an embodiment, tropical cyclone forecast data can be provided tosubscribers 671 via voice communication and/or conventional telephoneservice or devices 675, including facsimile machines 676. Informationcan also be received by the subscriber on a handheld or portable device682, such as cell phone or PDA.

Portions or all of the tropical cyclone forecast data may be transferredto an Internet or networked tropical cyclone prediction server 664. Thetropical cyclone prediction server 664 may be a simple PC, a web server,a combination of separate web server, application server, and databaseserver, or other arrangement of server resources. The Internet tropicalcyclone prediction server 664 could provide tropical cyclone forecastdata over the network 600 to other network systems or to PCs 666 withattached monitors 669 displaying Internet browsers or other applicationsoperated by users 668. The users 668 are similar to the subscribers 671,previously described. In another embodiment, the Internet tropicalcyclone prediction server 664 is accessed by mobile users 684 ofportable devices 682 via the wireless communication network 680.

The Internet tropical cyclone prediction server 664 could serve a webpage containing both HTML and JavaScript code. The JavaScript code couldperiodically, or upon user interaction, obtain additional or moreup-to-date tropical cyclone forecast data from the tropical cycloneprediction server 664 without reloading the web page. In one embodiment,the data is in XML form.

In another embodiment, tropical cyclone forecast data from the TCPS 100are also provided to Internet or network users 668. The tropical cycloneforecast data could be presented via a web-based interface through anInternet browser or customer application on the users' PCs 666 to allowinteractive exploration of the tropical cyclone forecast data. A user668 could enter the URL of a tropical cyclone prediction server 664. Theserver could attempt to distinguish the user's location from IP addressinformation, from a previously stored browser cookie, or from userinput.

The tropical cyclone forecast data may also be provided by the TCPS 100to a third-party server 674. In one embodiment, the subscriber 671 ofthe TCPS 100 could provide data to third-parties, who would then providevalue-added analysis or repackaging of the data.

In one embodiment, forecast data from the TCPS 100 is used bythird-parties to provide value-added services. For example, a searchengine operator may provide recent news or other information related toa tropical cyclone in addition to tropical cyclone forecast dataobtained from the TCPS 100 in response to weather-related keywords. Forexample, an Internet search for “hurricane tampa” could produce a map ofcurrent and/or predicted tropical cyclones impacting the Tampa area,along with information related to information about the tropicalcyclones obtained from the TCPS 100 and other information sources. Thegraphical results could be provided with regions responsive to furtheruser input, allowing the user to trigger display of additionalinformation about emergency planning or damage assessments. The searchcould be conducted on data transmitted to the search engine provider'sdatabase, or via calls to the Internet tropical cyclone predictionserver 664 or similar resource provided on the network 600.

FIG. 10 is a block diagram of a computer system 1000 through which theembodiments of the present invention may be implemented. A system bus1002 transports data amongst the Central Processing Unit (CPU) 1004, RAM1006, the Basic Input Output System (BIOS) 1008 and other components.The CPU 1004 may include a cache memory component 1024. The computersystem 1000 may include one or more external storage ports 1017 foraccessing a hard disk drive, optical storage drive (e.g., CD-ROM,DVD-ROM, DVD-RW), flash memory, tape device, or other storage device(not shown). The relevant storage device(s) are connected through theexternal storage port 1017 which is connected to the system bus 1002 viaa disk controller 1022. A keyboard and pointing device (e.g. mouse.touch pad) (not shown) can be connected to the keyboard/mouse port(s)1012, and other I/O devices could be connected to additional I/O port(s)1013, which are connected to the system bus 1002 through the I/Ocontroller 1010. Additional ports or devices, such as serial ports,parallel ports, firewire adapters, or biometric devices (not shown), maybe utilized through the I/O controller 1010A display device (not shown)can be connected to a display device port 1014 which is connected to thesystem bus 1002 through the video controller 1015. A network device (notshown), including but not limited to an Ethernet device or other devicehaving networking capability, can be connected to a network port 1020which is connected through the network controller 1016 to the system bus1002. The computer system 1000 may be wirelessly connected to a networkdevice that is configured for wireless operation (not shown), includingbut not limited to wireless routers, using an antenna 1028 connected toa wireless controller 1026 connected to the system bus 1002, where theantenna transmits/receives signals to/from the network device. Thecomputer system 1000 may include one or more USB ports 1023. A USBdevice (not shown), including but not limited to a printer, scanner,keyboard, mouse, digital camera, storage device, PDA, cellular phone,biometric device, webcam, and I/O adapters can be connected to the USBport 1023 which is connected to the system bus 1002 through the USBcontroller 1011. Other devices, such as cellular phones, PDAs, and otherportable devices may also be connected wirelessly via a wireless I/Oantenna 1032 that is connected to a wireless I/O controller 1030.Examples of wireless I/O technologies include, but are not limited to,Bluetooth, Infrared (IR), and Radio-Frequency (RF). Audio devices, suchas microphones, speakers, or headphones may be connected to a sound port1038 that is connected to a sound controller 1034 that is connected tothe system bus 1002. Expansion slots 1018 can be comprised of IndustryStandard Architecture (ISA) slots, Peripheral Component Interconnect(PCI) expansion slots, PCI Express expansion slots, Accelerated GraphicsPort (AGP) slots or any other slot generally known in the art to allowadditional cards to be placed into the computer system 1000. These slotscan be used to connect network cards, video cards, sound cards, modemsand any other peripheral devices generally used with a computer. Thecomputer system 1000 also includes a source of power (not shown),including but not limited to a power supply connected to an externalsource of power, and an internal or external battery. Detaileddescriptions of these devices have been omitted for convenience only andshould not be construed as limiting. The computer system 1000 shown inFIG. 10 can be part of the TCPS 100, or can be a processor present inanother element of the network 600.

The present invention may be implemented with any combination ofhardware and software. If implemented as a computer-implementedapparatus, the present invention is implemented using means forperforming all of the steps and functions described above.

The present invention can be included in an article of manufacture(e.g., one or more computer program products) having, for instance,computer useable media. The media has embodied therein, for instance,computer readable program code means for providing and facilitating themechanisms of the present invention. The article of manufacture can beincluded as part of a computer system or sold separately.

Although the description above contains many specific examples, theseshould not be construed as limiting the scope of the invention but asmerely providing illustrations of some of the presently preferredembodiments of this invention. Thus, the scope of the invention shouldbe determined by the appended claims and their legal equivalents, ratherthan by the examples given.

It will be appreciated by those skilled in the art that changes could bemade to the embodiments described above without departing from the broadinventive concept thereof. It is understood, therefore, that thisinvention is not limited to the particular embodiments disclosed, but itis intended to cover modifications within the spirit and scope of theembodiments of the present invention.

1. A method of predicting information related to a path of a weatherphenomenon, the method comprising: (a) obtaining a plurality of trackscorresponding to the weather phenomenon for a current forecast periodfrom two or more sources; (b) assigning a scaling factor to each of theplurality of tracks based on which of the two or more sources producedthe respective track; (c) determining, by at least one computer, a setof probabilities for the weather phenomenon to intersect a plurality ofsegments corresponding to a boundary using at least intersection pointsof the plurality of tracks with the boundary and the scaling factorassigned to each of the plurality of tracks.
 2. The method of claim 1,wherein determining the set of probabilities further includesstatistically analyzing the tracks that do not intersect the boundaryusing their assigned scaling factors.
 3. The method of claim 1, furthercomprising: (d) providing a graphical representation of the set ofprobabilities to at least one entity to facilitate forecasting an impactof the weather phenomenon.
 4. The method of claim 3, wherein theforecasted impact is a financial impact.
 5. The method of claim 3,wherein the forecasted impact is utilized to facilitate emergencypreparations related the weather phenomenon.
 6. The method of claim 3,wherein the graphical representation depicts a boundary intersectionfunction.
 7. The method of claim 6, wherein the integrated area underthe boundary intersection function is a total probability of the weatherphenomenon intersecting the boundary.
 8. The method of claim 3, whereinthe graphical representation is customized based on the needs of theentity by altering parameters for forming the plurality of segmentsalong the boundary.
 9. The method of claim 8, wherein the parameters forforming the plurality of segments include at least one of position alongthe boundary, length of each of the segments, and number of segments.10. The method of claim 1, wherein each probability of the set ofprobabilities corresponds to one of the plurality of segments.
 11. Themethod of claim 1, further comprising: (d) periodically repeating steps(a)-(c) using an updated plurality of tracks for the weather phenomenongenerated by the two or more sources.
 12. The method of claim 11,further comprising: (e) refining the scaling factors based on acomparison of at least some of the tracks for the weather phenomenonwith the actual path of the weather phenomenon.
 13. The method of claim1, further comprising: (d) estimating an uncertainty in the probabilityfor the segment with the greatest probability of intersection with amean track of the weather phenomenon using the plurality of tracksincluding the corresponding scaling factors from step (b).
 14. Themethod of claim 1, wherein the tracks include ensemble model tracksobtained from a forecast center.
 15. The method of claim 1, wherein thetracks include predictive tracks obtained from one or more short-rangemodels related to the weather phenomenon.
 16. The method of claim 1,wherein the weather phenomenon is a tropical cyclone.
 17. The method ofclaim 16, wherein a mean track of the tropical cyclone intersecting theplurality of segments includes the center of the tropical cyclone makinglandfall on the boundary of a geographic region.
 18. The method of claim17, wherein the boundary of the geographic region in a coastline. 19.The method of claim 1, wherein the scaling factor assigned to at leastone of the plurality of tracks is time varying.
 20. A method ofpredicting information related to a path of a tropical cycloneintersecting a boundary, the boundary being partitioned into a pluralityof segments, the method comprising: obtaining from each of a pluralityof forecast centers a plurality of model tracks corresponding to thetropical cyclone; assigning a scaling factor to each of the model tracksbased at least in part on the forecast center that produced the modeltrack, and weighting each of the model tracks according to thecorresponding scaling factor; determining for each of the plurality ofmodel tracks from the plurality of forecast centers, a segment from theplurality of segments where the respective model track intersects theboundary; calculating a segment value for each of the plurality ofsegments using a weighted average of a number of the plurality of modeltracks intersecting the respective segment; and determining, by at leastone computer, a probability for a mean track of the tropical cyclone tointersect each segment of the boundary using the segment value for therespective segment, wherein an uncertainty of the mean track of thetropical cyclone intersecting the boundary is proportional to a varianceof the intersection points for each of the plurality of model tracks atthe boundary, the contribution of each intersection point to thevariance based on the scaling factor for the respective model track. 21.The method of claim 20, further comprising: determining a set ofprobabilities related to a characteristic of the tropical cyclone basedon the associated scaling factor and values of the characteristic foreach model track at the intersection of the boundary.
 22. The method ofclaim 21, wherein the characteristic includes at least one of intensity,wind speed, minimum central pressure, tornado potential, precipitationrate, radius of impact, and storm surge height.
 23. The method of claim20, further comprising: aggregating the probabilities for each segmentto provide a graphical representation of the probabilities to at leastone entity to facilitate forecasting an impact of the tropical cyclone.24. The method of claim 23, wherein the forecasted impact is a financialimpact.
 25. The method of claim 20, wherein the scaling factors arebased in part on a historical accuracy of the corresponding forecastcenter in predicting actual tracks of tropical cyclones.
 26. The methodof claim 20, wherein the scaling factors are assigned to the modeltracks based at least in part on a geographic position of the tropicalcyclone.
 27. The method of claim 20, wherein the scaling factors areassigned to the model tracks based at least in part on characteristicsof the tropical cyclone.
 28. The method of claim 20, wherein the scalingfactors are assigned to the model tracks based at least in part on atleast one of atmospheric and ocean conditions affecting the tropicalcyclone.
 29. The method of claim 20, wherein the scaling factors areassigned to the model tracks based at least in part on the time of yearfor the occurrence of the tropical cyclone.
 30. The method of claim 20,wherein the scaling factor assigned to at least one of the model tracksis a time varying scaling factor, the time scaling varying factorincluding a different value of the factor for different positions of themodel track.
 31. A method of predicting information related to a path ofa tropical cyclone, the method comprising: (a) determining a set ofscaling factors corresponding to each of a plurality of forecastcenters; (b) assigning one of the scaling factors to each of a pluralityof model tracks for the tropical cyclone, the plurality of model tracksobtained for a current forecast period from the plurality of forecastcenters; (c) determining, by at least one computer, a probability for amean track of the tropical cyclone to intersect with each of a pluralityof segments defining a boundary based upon a statistical aggregating ofthe intersections of each of the model tracks with the boundary; and (d)estimating an uncertainty related to one or more segments ofintersection, wherein the uncertainty is based at least in part on theplurality of model tracks and the corresponding scaling factors.
 32. Themethod of claim 31, wherein the one or more segments of intersectionincludes the most probable segment of intersection.
 33. The method ofclaim 32, wherein the uncertainty related to the most probable segmentof intersection represents uncertainty in a position of most probablelandfall of the tropical cyclone.
 34. The method of claim 31, furthercomprising: (e) providing a graphical representation of the set ofprobabilities and the estimated uncertainty to at least one entity tofacilitate forecasting an impact of the tropical cyclone.
 35. The methodof claim 34, wherein the forecasted impact is a financial impact. 36.The method of claim 31, further comprising: (e) periodically repeatingsteps (a)-(d) using an updated plurality of model tracks for thetropical cyclone generated by the two or more forecast centers.
 37. Themethod of claim 31, further comprising: (e) determining a mean track forthe tropical cyclone using the plurality of model tracks andcorresponding scaling factors.
 38. The method of claim 37, wherein anindication of the most probable landfall position of the tropicalcyclone is determined by the intersection of the mean track and theboundary.
 39. An article of manufacture for predicting informationrelated to a path of a weather phenomenon intersecting a boundary, theboundary being partitioned into a plurality of segments, the article ofmanufacture comprising a non-transitory computer-readable medium holdingcomputer-executable instructions for performing a method comprising:obtaining from each of a plurality of forecast centers a plurality ofmodel tracks corresponding to the weather phenomenon; assigning ascaling factor to each of the model tracks based at least in part on theforecast center that produced the model track, and weighting each of themodel tracks according to the corresponding scaling factor; determiningfor each of the plurality of model tracks from the plurality of forecastcenters, a segment from the plurality of segments where the respectivemodel track intersects the boundary; calculating a segment value foreach of the plurality of segments using a weighted average of a numberof the plurality of model tracks intersecting the respective segment;and determining, by at least one computer, a probability for an meantrack of the weather phenomenon to intersect each segment of theboundary using the segment value for the respective segment, wherein anuncertainty of a forecast track of the tropical cyclone intersecting theboundary is proportional to a variance of the intersection points foreach of the plurality of model tracks at the boundary, the contributionof each intersection point to the variance based on the scaling factorfor the respective model track.